对于以下DataFrame：
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'Age': np.random.randint(20, 70, 100),
'Sex': np.random.choice(['Male', 'Female'], 100),
'number_of_foo': np.random.randint(1, 20, 100)})
df.head()
# Output:
# Age Sex number_of_foo
# 0 64 Female 14
# 1 67 Female 14
# 2 20 Female 12
# 3 23 Male 17
# 4 23 Female 15组Age分为三类（或容器）。垃圾箱可以给定为
n指示bin数的整数—在这种情况下，数据帧的数据分为n相等大小的间隔
整数表示左侧开区间，其中数据被分成-例如的终点的顺序bins=[19, 40, 65, np.inf]创建三个年龄组(19, 40]，(40, 65]和(65, np.inf]。
熊猫自动将间隔的字符串版本分配为标签。也可以通过将labels参数定义为字符串列表来定义自己的标签。
pd.cut(df['Age'], bins=4) # this creates four age groups: (19.951, 32.25] < (32.25, 44.5] < (44.5, 56.75] < (56.75, 69] Name: Age, dtype: category Categories (4, object): [(19.951, 32.25] < (32.25, 44.5] < (44.5, 56.75] < (56.75, 69]] pd.cut(df['Age'], bins=[19, 40, 65, np.inf]) # this creates three age groups: (19, 40], (40, 65] and (65, infinity) Name: Age, dtype: category Categories (3, object): [(19, 40] < (40, 65] < (65, inf]]
用它groupby来获取foo的平均数：
age_groups = pd.cut(df['Age'], bins=[19, 40, 65, np.inf]) df.groupby(age_groups)['number_of_foo'].mean() # Output: # Age # (19, 40] 9.880000 # (40, 65] 9.452381 # (65, inf] 9.250000 # Name: number_of_foo, dtype: float64
交叉列出年龄段和性别：
pd.crosstab(age_groups, df['Sex']) # Output: # Sex Female Male # Age # (19, 40] 22 28 # (40, 65] 18 24 # (65, inf] 3 5